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Changchun University of Technology

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Selected work

Representative Papers

Distributed Selective Inference for Quantile Regression

Aug 13, 2026

This work addresses the computational and statistical challenges of post-selection inference in high-dimensional quantile regression by proposing the first distributed selective inference framework. The method innovatively integrates a response proxy strategy with randomized Lasso to transform the nonsmooth quantile loss into a penalized least squares problem. By precisely characterizing the selection event via KKT conditions, it enables efficient inference with only three rounds of communication. Under standard regularity conditions, the asymptotic validity of the proposed inference procedure is rigorously established. Extensive simulations and empirical analyses further demonstrate its superior finite-sample performance.

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Distributed Convolutional Rank Regression over Decentralized Networks

Jul 26, 2026

This work addresses the challenges of data heterogeneity and communication efficiency in decentralized networks by proposing a privacy-preserving distributed Convolutional Rank Regression (CRR) estimation method. The approach leverages a kernel-smoothed rank loss combined with consensus constraints, enabling each node to train its model using only local data and information from neighboring nodes. An efficient solution is achieved via a generalized consensus ADMM algorithm. Theoretically, this study establishes the first finite-sample error bounds for decentralized CRR and provides sharp support recovery guarantees for sparse CRR LASSO estimators. Experimental results demonstrate that the proposed method consistently outperforms existing approaches in terms of estimation accuracy, communication overhead, and privacy preservation.

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Optimal Poisson subsampling for quantile regression with large-scale longitudinal data

Jun 22, 2026

This study addresses the high computational cost of quantile regression for large-scale longitudinal data by proposing an efficient estimation method based on optimal Poisson subsampling. For the first time, optimal Poisson subsampling is integrated into the longitudinal quantile regression framework, combined with a weighted smoothed quantile generalized estimating equation and regularization techniques to achieve sparse parameter estimation. The authors establish the corresponding asymptotic theory to support the proposed approach. Numerical experiments and real data analysis demonstrate that the method significantly outperforms uniform Poisson subsampling in both estimation accuracy and computational efficiency, while the regularized estimator exhibits strong variable selection performance.

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Recent publications

Latest Papers

Distributed Selective Inference for Quantile Regression

Aug 13, 2026

This work addresses the computational and statistical challenges of post-selection inference in high-dimensional quantile regression by proposing the first distributed selective inference framework. The method innovatively integrates a response proxy strategy with randomized Lasso to transform the nonsmooth quantile loss into a penalized least squares problem. By precisely characterizing the selection event via KKT conditions, it enables efficient inference with only three rounds of communication. Under standard regularity conditions, the asymptotic validity of the proposed inference procedure is rigorously established. Extensive simulations and empirical analyses further demonstrate its superior finite-sample performance.

0 citationsRead paper

Distributed Convolutional Rank Regression over Decentralized Networks

Jul 26, 2026

This work addresses the challenges of data heterogeneity and communication efficiency in decentralized networks by proposing a privacy-preserving distributed Convolutional Rank Regression (CRR) estimation method. The approach leverages a kernel-smoothed rank loss combined with consensus constraints, enabling each node to train its model using only local data and information from neighboring nodes. An efficient solution is achieved via a generalized consensus ADMM algorithm. Theoretically, this study establishes the first finite-sample error bounds for decentralized CRR and provides sharp support recovery guarantees for sparse CRR LASSO estimators. Experimental results demonstrate that the proposed method consistently outperforms existing approaches in terms of estimation accuracy, communication overhead, and privacy preservation.

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Optimal Poisson subsampling for quantile regression with large-scale longitudinal data

Jun 22, 2026

This study addresses the high computational cost of quantile regression for large-scale longitudinal data by proposing an efficient estimation method based on optimal Poisson subsampling. For the first time, optimal Poisson subsampling is integrated into the longitudinal quantile regression framework, combined with a weighted smoothed quantile generalized estimating equation and regularization techniques to achieve sparse parameter estimation. The authors establish the corresponding asymptotic theory to support the proposed approach. Numerical experiments and real data analysis demonstrate that the method significantly outperforms uniform Poisson subsampling in both estimation accuracy and computational efficiency, while the regularized estimator exhibits strong variable selection performance.

0 citationsRead paper